Dedicated teams to optimize quality and safety of surgery: A systematic review.

BACKGROUND: A dedicated operating team is defined as a surgical team consisting of the same group of people working together over time, optimally attuned in both technical and/or communicative aspects. This can be achieved through technical and/or communicative training in a team setting. A dedicated surgical team may contribute to the optimization of healthcare quality and patient safety within the perioperative period. METHOD: A systematic review was conducted to evaluate the effects of a dedicated surgical team on clinical and performance outcomes. MEDLINE and Embase were searched on 23 June 2022. Both randomized controlled trials (RCTs) and non-randomized studies (NRSs) were included. Primary outcomes were mortality, complications and readmissions. Secondary outcomes were costs and performance measures. RESULTS: Fourteen studies were included (RCTs n = 1; NRSs n = 13). Implementation of dedicated operating teams was associated with improvements in mortality, turnover time, teamwork, communication and costs. No significant differences were observed in readmission rates and length of hospital stay. Results regarding duration, glitch counts and complications of surgery were inconclusive. Limitations include study conduct and heterogeneity between studies. CONCLUSIONS: The institution of surgical teams who followed communicative and/or technical training appeared to have beneficial effects on several clinical outcome measures. Dedicated teams provide a feasible way of improving healthcare quality and patient safety. A dose-response effect of team training was reported, but also a relapse rate, suggesting that repetitive training is of major concern to high-quality patient care. Further studies are needed to confirm these findings, due to limited level of evidence in current literature. PROSPERO REGISTRATION NUMBER: CRD42020145288.

Nutritional status and out-of-hospital mortality in vascular surgery patients.

BACKGROUND: Malnutrition is often present in vascular surgery patient during hospital admission. The present evidence of the consequence malnutrition has on morbidity and mortality is limited. AIM: The purpose of this study was to determine the effect of nutritional status on out-of-hospital mortality in vascular surgery patients. METHODS: An observational cohort study was performed, studying non-cardiac vascular surgery patients surviving hospital admission 18 years or older treated in Boston, Massachusetts, USA. The exposure of interest was nutritional status categorized as well nourished, at-risk for malnutrition, nonspecific malnutrition or protein-energy malnutrition. The all cause 90-day mortality following hospital discharge was the primary outcome. Adjusted odds ratios were estimated by multivariable logistic regression models. RESULTS: This cohort included 4432 patients comprised of 48% women and a mean age 61.7 years. After evaluation by a registered dietitian, 3819 patients were determined to be well nourished, 215 patients were at-risk for malnutrition, 351 had non-specific malnutrition and 47 patients had protein-energy malnutrition. After adjustment for age, sex, ethnicity, medical versus surgical Diagnosis Related Group type, Deyo-Charlson index, length of stay, and vascular Current Procedural Terminology code category, the 90-day post-discharge mortality odds ratio for patients with non-specific malnutrition OR 1.96 (95%CI 1.21, 3.17) and for protein-energy malnutrition OR 3.58 (95%CI 1.59, 8.06), all relative to patients without malnutrition. DISCUSSION: Nutritional status is a strong predictor of out-of-hospital mortality. This suggests that patient with vascular disease suffering from malnutrition could benefit from more intensified In-hospital and out-of-hospital dietary guidance and interventions.

Eosinopenia and post-hospital outcomes in critically ill non-cardiac vascular surgery patients.

BACKGROUND AND AIMS: Eosinopenia is a marker for acute inflammation. We hypothesized that eosinopenia at Intensive Care Unit (ICU) admission in vascular surgery patients who receive critical care, would be associated with increased mortality following hospital discharge. METHODS AND RESULTS: We performed a two-center observational cohort study of critically ill, non-cardiac adult vascular surgery patients who received treatment in Boston between 1997 and 2012 and survived hospital admission. The consecutive sample included 5083 patients (male 57%, white 82%, mean age [SD] 61.6 [17.4] years). The exposure was Absolute eosinophil count measured within 24 h of admission to the ICU and categorized as ≤10 cells/µL, 11-50 cells/µL, 51-100 cells/µL, 101-350 cells/µL (normal range), and >350 cells/µL. The primary outcome was all-cause mortality within 90 days of hospital discharge. The secondary outcome was discharge to home following hospitalization. 90-day post-discharge mortality was 6.7%, and 12.9% of patients were readmitted within 30 days. After multivariable adjustment, patients with eosinopenia (≤10 cells/µL) have a 90-day post-discharge mortality OR of 1.97 (95%CI 1.42, 2.73; P < 0.001) relative to patients with an absolute eosinophil count of 101-350 cells/µL. Further, after multivariable adjustment, patients with eosinopenia (≤10 cells/µL) have a 25% lower odds of discharge to home compared to patients with an absolute eosinophil count of 101-350 cells/µL [OR = 0.71 (CI 95% 0.59-0.85); P < 0.001]. CONCLUSION: Eosinopenia at ICU admission is a robust predictor of increased mortality and lower likelihood of discharge to home in vascular surgery patients treated with critical care who survive hospitalization.

Development and External Validation of a Model Predicting Death After Surgery in Patients With a Ruptured Abdominal Aortic Aneurysm: The Dutch Aneurysm Score.

OBJECTIVE: The decision whether or not to proceed with surgical intervention of a patient with a ruptured abdominal aortic aneurysm (rAAA) is very difficult in daily practice. The primary objective of the present study was to develop and to externally validate a new prediction model: the Dutch Aneurysm Score (DAS). METHODS: With a prospective cohort of 10 hospitals (n = 508) the DAS was developed using a multivariate logistic regression model. Two retrospective cohorts with rAAA patients from two hospitals (n = 373) were used for external validation. The primary outcome was the combined 30 day and in-hospital death rate. Discrimination (AUC), calibration plots, and the ability to identify high risk patients were compared with the more commonly used Glasgow Aneurysm Score (GAS). RESULTS: After multivariate logistic regression, four pre-operative variables were identified: age, lowest in hospital systolic blood pressure, cardiopulmonary resuscitation, and haemoglobin level. The area under the receiver operating curve (AUC) for the DAS was 0.77 (95% CI 0.72-0.82) compared with the GAS with an AUC of 0.72 (95% CI 0.67-0.77). The DAS showed a death rate in patients with a predicted death rate ≥80% of 83%. CONCLUSIONS: The present study shows that the DAS has a higher discriminative performance (AUC) compared with the GAS. All clinical variables used for the DAS are easy to obtain. Identification of low risk patients with the DAS can potentially reduce turndown rates. The DAS can reliably be used by clinicians to make a more informed decision in dialogue with the patient and their family whether or not to proceed with surgical intervention.

Estimation and testing in targeted goup sequential covariate-adjusted randomized clinical trials.

This article is devoted to the construction and asymptotic study of adaptive group sequential covariate-adjusted randomized clinical trials analyzed through the prism of the semipara-metric methodology of targeted maximum likelihood estimation (TMLE). We show how to build, as the data accrue group-sequentially, a sampling design which targets a user-supplied optimal design. We also show how to carry out a sound TMLE statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the i.i.d setting only so far), and how group-sequential testing applies on top of it. The procedure is robust (i.e., consistent even if the working model is misspecified). A simulation study confirms the theoretical results, and validates the conjecture that the procedure may also be efficient.

Direct effects of leisure-time physical activity on walking speed.

OBJECTIVE: This study quantifies the effects of leisure-time physical activity (LTPA) on walking speed independently of body composition in an elderly cohort, and in those elderly with metabolic derangements due to age, diabetes, and cardiovascular disease (CVD). METHODS: 1655 community-dwelling women and men >55 years were measured for body composition (lean mass : fat mass ratio, LNFAT) , based on estimated bioelectric impedance by using population-specific prediction equations derived from dual-energy x-ray absorptiometry. In addition to LNFAT, LTPA, diabetes, CVD, walking speed, and other covariates were measured biannually over an 8-year period. LTPA was categorized as <22.5 Mets/week, ≥ 22.5 Mets/week, based on public-health recommended guidelines, and LNFAT was dichotomized based on its sex-specific median. Direct effects of high vs. low LTPA on walking speed were estimated for fixed levels of LNFAT, which represented an intermediary variable in the analysis. Stratified estimates of effects were obtained using subject status (e.g., age≥75 years, diabetes, CVD) at each visit. RESULTS: Walking speed was significantly greater (0.74, 0.75 m/s in women and men, respectively) if subjects experienced LTPA ≥22.5 Mets/week and > median LNFAT, compared with <22.5 Mets/week and ≤ median LNFAT (0.68, 0.69 m/s). While direct effects of LTPA contributed to higher walking speed, none were significant in the overall, nor the stratified groups of subjects, of either sex. CONCLUSIONS: Walking speed increases with greater LTPA and LNFAT in the elderly, but there was no evidence to indicate that walking speed increases from LTPA independently of body composition and the metabolic processes it represents.

Systematic review and meta-analysis of population-based mortality from ruptured abdominal aortic aneurysm.

BACKGROUND: A substantial proportion of patients with a ruptured abdominal aortic aneurysm (rAAA) die outside hospital. The objective of this study was to estimate the total mortality, including prehospital deaths, of patients with rAAA. METHODS: This was a systematic review and meta-analysis following the MOOSE guidelines. The Embase, MEDLINE and Cochrane Library databases were searched. All population-based studies reporting both prehospital and in-hospital mortality in patients with rAAA were included. Studies were assessed for methodological quality and heterogeneity, and pooled estimates of mortality from rAAA were calculated using a random-effects model. RESULTS: From a total of 3667 studies, 24 retrospective cohort studies, published between 1977 and 2012, met the inclusion criteria. The quality of included studies varied, in particular the method of determining prehospital deaths from rAAA. The estimated pooled total mortality rate was 81 (95 per cent confidence interval 78 to 83) per cent. A decline in mortality was observed over time (P = 0·002); the pooled estimate of total mortality in high-quality studies before 1990 was 86 (83 to 89) per cent, compared with 74 (72 to 77) per cent since 1990. Some 32 (27 to 37) per cent of patients with rAAA died before reaching hospital. The in-hospital non-intervention rate was 40 (33 to 47) per cent, which also declined over the years. CONCLUSION: The pooled estimate of total mortality from rAAA is very high, although it has declined over the years. Most patients die outside hospital, and there is no surgical intervention in a considerable number of those who survive to reach hospital.

Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment.

It is a challenge to evaluate experimental treatments where it is suspected that the treatment effect may only be strong for certain subpopulations, such as those having a high initial severity of disease, or those having a particular gene variant. Standard randomized controlled trials can have low power in such situations. They also are not optimized to distinguish which subpopulations benefit from a treatment. With the goal of overcoming these limitations, we consider randomized trial designs in which the criteria for patient enrollment may be changed, in a preplanned manner, based on interim analyses. Since such designs allow data-dependent changes to the population enrolled, care must be taken to ensure strong control of the familywise Type I error rate. Our main contribution is a general method for constructing randomized trial designs that allow changes to the population enrolled based on interim data using a prespecified decision rule, for which the asymptotic, familywise Type I error rate is strongly controlled at a specified level α. As a demonstration of our method, we prove new, sharp results for a simple, two-stage enrichment design. We then compare this design to fixed designs, focusing on each design's ability to determine the overall and subpopulation-specific treatment effects.

Covariate adjustment in randomized trials with binary outcomes: targeted maximum likelihood estimation.

Covariate adjustment using linear models for continuous outcomes in randomized trials has been shown to increase efficiency and power over the unadjusted method in estimating the marginal effect of treatment. However, for binary outcomes, investigators generally rely on the unadjusted estimate as the literature indicates that covariate-adjusted estimates based on the logistic regression models are less efficient. The crucial step that has been missing when adjusting for covariates is that one must integrate/average the adjusted estimate over those covariates in order to obtain the marginal effect. We apply the method of targeted maximum likelihood estimation (tMLE) to obtain estimators for the marginal effect using covariate adjustment for binary outcomes. We show that the covariate adjustment in randomized trials using the logistic regression models can be mapped, by averaging over the covariate(s), to obtain a fully robust and efficient estimator of the marginal effect, which equals a targeted maximum likelihood estimator. This tMLE is obtained by simply adding a clever covariate to a fixed initial regression. We present simulation studies that demonstrate that this tMLE increases efficiency and power over the unadjusted method, particularly for smaller sample sizes, even when the regression model is mis-specified.

Increasing power in randomized trials with right censored outcomes through covariate adjustment.

Targeted maximum likelihood methodology is applied to provide a test that makes use of the covariate data that are commonly collected in randomized trials, and does not require assumptions beyond those of the logrank test when censoring is uninformative. Under informative censoring, the logrank test is biased, whereas the test provided in this article is consistent under consistent estimation of the censoring mechanism or the conditional hazard for survival. Two approaches based on this methodology are provided: (1) a substitution-based approach that targets treatment and time-specific survival from which the logrank parameter is estimated, and (2) directly targeting the logrank parameter.

Pericardial cyst located in the anterior mediastinum mimicking a thymoma. A presentation of two cases.

Pericardial cysts are rare but well recognized tumors of the mediastinum. Most pericardial cysts are located in the right or left cardiophrenic angle. At other locations these cysts may pose a diagnostic problem. We present two cases of an atypically located pericardial cyst and a short review of the literature.

Computed tomography versus magnetic resonance imaging of endoleaks after EVAR.

AIM: The aim of study was to compare the sensitivity of MRI and CTA for endoleak detection and classification after EVAR. PATIENTS & METHODS: Twenty-eight patients, between 2 days and 65 months after EVAR, were evaluated with both CT and MRI. Twenty-five patients had an Ancure graft and the other three had an Excluder. The MRI protocol for endoleak evaluation included: a T1-weighted spin echo, a high-resolution 3D CE-MRA, and a post-contrast T1-weighted spin echo. In total 40 ml Gadolinium was administered. The CT protocol consisted of a blank survey followed by a spiral CT angiography (CTA) using 140 ml of Ultravist. An experienced, blinded observer evaluated all CTs and MRIs. RESULTS: Using MRI and MRA techniques significantly more endoleaks (23/35) were detected than with CTA (11/35) (p=0.01, Chi-Square). CT could not determine the type of endoleak in 3 of the 11 endoleaks detected and was uncertain in one. MRI was uncertain about the type in 14 of the 23 endoleaks detected. All endoleaks visible on CT were visible by MRI as well. CONCLUSIONS: MRI techniques are more sensitive for the detection of endoleak after endovascular AAA repair than CT.

Dynamic CE-MRA for endoleak classification after endovascular aneurysm repair.

AIM: To evaluate the value of dynamic contrast enhanced magnetic resonance angiography (CE-MRA) for classification of endoleaks after endovascular aneurysm repair (EVAR). MATERIALS AND METHODS: Twenty-eight patients, between 2 days and 54 months after EVAR, were evaluated with CTA, MRI and dynamic CE-MRA. The additional diagnostic value of the dynamic 3D CE-MRA was evaluated by determining the ability of the dynamic series in pinpointing the site of inflow of an endoleak. RESULTS: An endoleak was detected in 23 patients. Seventeen of the 23 dynamic series were technically successful (no disturbing artifacts limiting the diagnostic value). Using MRI our findings were: 2 type I, 6 type II, 1 type III, no type IV endoleaks and in 14 cases classification could not be made. The classification results for MRI plus the dynamic CE-MRA were: 2 type I, 12 type II, 1 type III, no type IV endoleaks and in eight cases classification could not be made. In six cases the dynamic MRA allowed classification of the endoleak, which was not possible with the non-dynamic images alone (p=0.091, Fisher exact). CONCLUSION: This pilot study shows that dynamic CE-MRA can have additional value in the classification of endoleaks. Dynamic CE-MRA might obviate the need for diagnostic digital subtraction angiography and aid planning for intervention.

Short- and long-term effects of neonatal glucocorticoid therapy: is hydrocortisone an alternative to dexamethasone?

AIM: To compare short-term effects and neurodevelopmental outcome of neonatal glucocorticoid therapy between two centres. METHODS: A retrospective study was performed in two centres using a tapering course of either 5 to 1 mg kg(-1) hydrocortisone (HC; 22 d) or 0.5 to 0.1 mg kg(-1) dexamethasone (DEX; 21 d). In both centres glucocorticoid-treated infants and control patients were matched for gestational age, birthweight, severity of infant respiratory distress syndrome and periventricular-intraventricular haemorrhage. The following short-term glucocorticoid-induced effects were investigated in 25 HC-treated and 25 control patients in centre A, and in 23 DEX-treated and 23 control patients in centre B: oxygen dependency (inspiratory oxygen fraction), arterial pressure, blood glucose and urea concentrations, weight gain and head circumference before, during and after therapy (in treated infants), or at an interval comparable to treated infants (in control infants). Neurological outcome, psychomotor development and school performance at 5-7 y of age was evaluated in all groups. RESULTS: HC and DEX were equally potent in reducing oxygen dependency. Mean arterial pressure as well as blood glucose and urea concentrations were significantly increased during DEX, but not during HC treatment. Weight gain stopped during DEX therapy, but not during HC. Head circumference in both treatment groups was decreased after therapy compared with controls. Neonatally DEX-treated children needed special school education significantly more often (p < 0.01) than controls at 5-7 y of age. No differences between neonatally HC-treated children and controls on neurodevelopmental outcome were found at 5-7 y of age. CONCLUSION: Neonatal HC therapy has fewer short- and long-term adverse effects than neonatal DEX therapy.

Gene expression analysis with the parametric bootstrap.

Recent developments in microarray technology make it possible to capture the gene expression profiles for thousands of genes at once. With this data researchers are tackling problems ranging from the identification of 'cancer genes' to the formidable task of adding functional annotations to our rapidly growing gene databases. Specific research questions suggest patterns of gene expression that are interesting and informative: for instance, genes with large variance or groups of genes that are highly correlated. Cluster analysis and related techniques are proving to be very useful. However, such exploratory methods alone do not provide the opportunity to engage in statistical inference. Given the high dimensionality (thousands of genes) and small sample sizes (often <30) encountered in these datasets, an honest assessment of sampling variability is crucial and can prevent the over-interpretation of spurious results. We describe a statistical framework that encompasses many of the analytical goals in gene expression analysis; our framework is completely compatible with many of the current approaches and, in fact, can increase their utility. We propose the use of a deterministic rule, applied to the parameters of the gene expression distribution, to select a target subset of genes that are of biological interest. In addition to subset membership, the target subset can include information about relationships between genes, such as clustering. This target subset presents an interesting parameter that we can estimate by applying the rule to the sample statistics of microarray data. The parametric bootstrap, based on a multivariate normal model, is used to estimate the distribution of these estimated subsets and relevant summary measures of this sampling distribution are proposed. We focus on rules that operate on the mean and covariance. Using Bernstein's Inequality, we obtain consistency of the subset estimates, under the assumption that the sample size converges faster to infinity than the logarithm of the number of genes. We also provide a conservative sample size formula guaranteeing that the sample mean and sample covariance matrix are uniformly within a distance epsilon > 0 of the population mean and covariance. The practical performance of the method using a cluster-based subset rule is illustrated with a simulation study. The method is illustrated with an analysis of a publicly available leukemia data set.

Marginal Mean Models for Dynamic Regimes.

A dynamic treatment regime is a list of rules for how the level of treatment will be tailored through time to an individual's changing severity. In general, individuals who receive the highest level of treatment are the individuals with the greatest severity and need for treatment. Thus there is planned selection of the treatment dose. In addition to the planned selection mandated by the treatment rules, the use of staff judgment results in unplanned selection of the treatment level. Given observational longitudinal data or data in which there is unplanned selection, of the treatment level, the methodology proposed here allows the estimation of a mean response to a dynamic treatment regime under the assumption of sequential randomization.

Nonparametric survival estimation when death is reported with delay.

In disease registries there can be a delay between death of a subject and the reporting of this death to the data analyst. If researchers use the Kaplan-Meier estimator and implicitly assumed that subjects who have yet to have death reported are still alive, i.e. are censored at the time of analysis, the Kaplan-Meier estimator is typically inconsistent. Assuming censoring is independent of failure, we provide a simple estimator that is consistent and asymptotically efficient. We also provide estimates of the asymptotic variance of our estimator and simulations that demonstrate the favorable performance of these estimators. Finally, we demonstrate our methods by analyzing AIDS survival data. This analysis underscores the pitfalls of not accounting for delay when estimating the survival distribution and suggests a significant reduction in bias by using our estimator.